I started part1 a few days back. I was trying to see if I can run lesson1 on my laptop. I had already installed Python3, Cuda, cuDNN, Keras, TensorFlow and more when I did the "getting started" section of TensorFlow. I tweaked some .py scripts in the repo to get it work with Python3 and Keras2.X.X and I think I got it working. I then hit a lot of issues that boil down to insufficient memory. The GPU on my laptop doesn't have decent spec. It has 2GB of memory. I tried reducing batch size to 4 and still got error "Dst tensor is not initialized". This error also seems to stem from out of memory.

So question, is there a minimum recommended hardware for this tutorial? Are there any Keras/TF settings that I can change to help.

I've created an AWS account and requested for p2 to move forward. But I still want to see if I can tinker a bit and get this to run on my laptop!!

Hi!Here's a very nice article by Tim Dettmers that details and compares GPUs for Deep Learning.Maybe you already know it but it might help in this discussion.

And for my personal touch, I started playing with VGG16 on a GTX750ti. It was not really fast, but the pre-trained model fits in the 2GB memory of that card and allows for a batch size of 4 to 8 on Cats vs. Dogs redux (shape 3, 224, 224).I switched to a GTX1070 later and the training is roughly 5-6x faster, and 8GB VRAM fits up to ~180 images (still on Cats vs. Dogs redux, same shape).

I would say that you can give it a try if you already have a 2GB card, but it will be limited with VGG16 (which is the largest of the pre-trained models available on Keras, so it might be easier playing with others in a second time).Any 4-6GB card should allow you to go through Part 1 without problem, and have fun on some Kaggle competitions.If you can go for a 1070 or upper, you should have to upgrade before tackling really serious tasks.